1 Package Installing

I install some packages before i start out analysis.

library(readxl)
library(ggplot2)
library(dplyr)
library(tidyr)
library(lubridate)
library(RColorBrewer)
library(zoo)
library(data.table)
library(plotly)

2 Data Information

I take four datasets from EVDS. These are: “-House Sales Statistics - Turkey-House Sales Statistics - Mortgaged sales”, “-Housing (TRY)-Weighted Average Interest Rates For Banks Loans”, “-Financial situation of household expectation - Seasonally unadjusted Consumer Confidence Index and Indices of Consumer Tendency Survey Questions”, “-Dollar Exchange Rate”

After receiving the data, i manipulated the data.

## [1] 0
## [1] 0
## Rows: 48
## Columns: 2
## $ Date        <date> 2017-04-01, 2017-05-01, 2017-06-01, 2017-07-01, 2017-0...
## $ Expenditure <dbl> 43334, 41738, 35210, 38575, 38743, 40534, 38593, 37250,...
## [1] 0
## [1] 0
## Rows: 48
## Columns: 2
## $ Date <date> 2017-04-01, 2017-05-01, 2017-06-01, 2017-07-01, 2017-08-01, 2...
## $ Rate <dbl> 3.653835, 3.563862, 3.518990, 3.559867, 3.512477, 3.468047, 3....
## [1] 0
## [1] 0
## Rows: 48
## Columns: 2
## $ Date     <date> 2017-04-01, 2017-05-01, 2017-06-01, 2017-07-01, 2017-08-0...
## $ Interest <dbl> 11.2750, 11.5450, 11.7200, 12.0150, 12.4900, 12.8680, 13.0...
## [1] 0
## [1] 0
## Rows: 48
## Columns: 3
## $ Date        <date> 2017-04-01, 2017-05-01, 2017-06-01, 2017-07-01, 2017-0...
## $ Real        <dbl> 83.67062, 85.64057, 85.20785, 86.94195, 86.24911, 85.71...
## $ Expectation <dbl> 90.73988, 91.98471, 89.96299, 91.85820, 91.86267, 89.55...

3 Visualiton of Datasets

ggplotly(
ggplot(data=house_sales_new, aes(x=Date, y=Expenditure)) +
  geom_line(color="red")+
  geom_point()+theme(plot.title = element_text(hjust = 0.5))+
  labs(
    title = "House Sales Statistics - Turkey - Mortgaged sales",
    x = "Date",
    y = "Expenditure"
  ))
ggplotly(
ggplot(data=dollar_buying_new, aes(x=Date, y=Rate, group=1)) +
  geom_line(color="red")+
  geom_point()+theme(plot.title = element_text(hjust = 0.5))+
  labs(
    title = "Dollar Exchange Rate",
    x = "Date",
    y = "Rate"
  ))
ggplot(data=household_new, aes(x=Date, y=Expectation, group=1))+
  geom_line(color="red")+
  geom_point()+theme(plot.title = element_text(hjust = 0.5))+
  labs(
    title = "Financial Situation of Household Expectation - Survey",
    x = "Date",
    y = "Interest"
)

ggplot(data=household_new, aes(x=Date, y=Real, group=1))+
  geom_line(color="red")+
  geom_point()+theme(plot.title = element_text(hjust = 0.5))+
  labs(
    title = "Financial Situation of Household-Survey",
    x = "Date",
    y = "Real"
)

ggplotly(
ggplot(data=housing_interest_new, aes(x=Date, y=Interest, group=1)) +
  geom_line(color="red")+
  geom_point()+theme(plot.title = element_text(hjust = 0.5))+
  labs(
    title = "I.R for Bank Loans",
    x = "Date",
    y = "Interest"
  ))

4 Modelling

# sort each with date

house_sales_new<-house_sales_new[order(Date),]
household_new<-household_new[order(Date),]
housing_interest_new<-housing_interest_new[order(Date),]
dollar_buying_new<-dollar_buying_new[order(Date),]
# sort each with date

lag.count<-6
take.year.lag<-function(array_){
  df<-c()
  for (i in 1:lag.count){
    df<-cbind(df,lag(array_,n=i))
  }
  return(df)
}


house_sales_new_lags=take.year.lag(house_sales_new$Expenditure)
lag_col_names=c()
for (i in 1:lag.count){
  lag_col_names=append(lag_col_names,paste0("Expenditure_lag_",i))
}
colnames(house_sales_new_lags)=lag_col_names

household_new_lags.Real=take.year.lag(household_new$Real)
lag_col_names=c()
for (i in 1:lag.count){
  lag_col_names=append(lag_col_names,paste0("Real_lag_",i))
}
colnames(household_new_lags.Real)=lag_col_names

household_new_lags.Expectation=take.year.lag(household_new$Expectation)
lag_col_names=c()
for (i in 1:lag.count){
  lag_col_names=append(lag_col_names,paste0("Expectation_lag_",i))
}
colnames(household_new_lags.Expectation)=lag_col_names

housing_interest_new_lags=take.year.lag(housing_interest_new$Interest)
lag_col_names=c()
for (i in 1:lag.count){
  lag_col_names=append(lag_col_names,paste0("Interest_lag_",i))
}
colnames(housing_interest_new_lags)=lag_col_names

dollar_buying_new_lags=take.year.lag(dollar_buying_new$Rate)
lag_col_names=c()
for (i in 1:lag.count){
  lag_col_names=append(lag_col_names,paste0("Rate_lag_",i))
}
colnames(dollar_buying_new_lags)=lag_col_names
house_sales_main<-cbind(house_sales_new,house_sales_new_lags,household_new_lags.Real,household_new_lags.Expectation,housing_interest_new_lags,dollar_buying_new_lags)
house_sales_main<-na.omit(house_sales_main)

house_sales_main$Year<-as.numeric(format(house_sales_main$Date, format="%Y"))
house_sales_main$Month<-as.numeric(format(house_sales_main$Date, format="%m"))

model<-lm(Expenditure~.-Date,data=house_sales_main)
summary(model)
## 
## Call:
## lm(formula = Expenditure ~ . - Date, data = house_sales_main)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7978.5 -2282.9   -98.9  2347.9  9506.3 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        9.351e+07  7.068e+07   1.323   0.2185  
## Expenditure_lag_1  8.356e-01  3.430e-01   2.436   0.0376 *
## Expenditure_lag_2 -6.066e-01  4.142e-01  -1.464   0.1771  
## Expenditure_lag_3  2.393e-01  4.081e-01   0.586   0.5720  
## Expenditure_lag_4 -7.190e-01  3.871e-01  -1.857   0.0962 .
## Expenditure_lag_5 -4.162e-01  4.165e-01  -0.999   0.3438  
## Expenditure_lag_6 -1.182e-02  2.942e-01  -0.040   0.9688  
## Real_lag_1        -1.376e+03  1.741e+03  -0.790   0.4496  
## Real_lag_2         4.573e+03  1.811e+03   2.525   0.0325 *
## Real_lag_3         5.781e+02  2.164e+03   0.267   0.7954  
## Real_lag_4        -4.167e+03  2.859e+03  -1.457   0.1790  
## Real_lag_5        -4.707e+03  1.768e+03  -2.662   0.0260 *
## Real_lag_6         4.793e+03  1.773e+03   2.703   0.0243 *
## Expectation_lag_1  2.412e+03  1.110e+03   2.173   0.0579 .
## Expectation_lag_2 -3.048e+03  1.115e+03  -2.733   0.0231 *
## Expectation_lag_3 -2.269e+03  1.211e+03  -1.873   0.0939 .
## Expectation_lag_4 -2.167e+01  1.865e+03  -0.012   0.9910  
## Expectation_lag_5  2.755e+03  1.512e+03   1.822   0.1018  
## Expectation_lag_6 -2.748e+03  1.310e+03  -2.097   0.0654 .
## Interest_lag_1    -2.390e+03  1.925e+03  -1.242   0.2458  
## Interest_lag_2     9.913e+02  2.652e+03   0.374   0.7172  
## Interest_lag_3     3.701e+03  2.596e+03   1.426   0.1877  
## Interest_lag_4    -4.172e+03  2.951e+03  -1.414   0.1910  
## Interest_lag_5    -6.374e+03  2.751e+03  -2.317   0.0457 *
## Interest_lag_6     1.189e+03  1.743e+03   0.682   0.5122  
## Rate_lag_1         2.524e+04  2.059e+04   1.226   0.2513  
## Rate_lag_2         1.528e+04  2.087e+04   0.732   0.4827  
## Rate_lag_3        -3.366e+04  2.214e+04  -1.520   0.1628  
## Rate_lag_4        -5.507e+02  2.548e+04  -0.022   0.9832  
## Rate_lag_5         2.874e+03  2.383e+04   0.121   0.9066  
## Rate_lag_6         2.433e+04  1.733e+04   1.404   0.1939  
## Year              -4.618e+04  3.498e+04  -1.320   0.2194  
## Month             -3.068e+03  3.349e+03  -0.916   0.3835  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7966 on 9 degrees of freedom
## Multiple R-squared:  0.9778, Adjusted R-squared:  0.899 
## F-statistic:  12.4 on 32 and 9 DF,  p-value: 0.0002164
final.model<-step(lm(Expenditure~.-Date , data=house_sales_main),direction="backward")
## Start:  AIC=755.87
## Expenditure ~ (Date + Expenditure_lag_1 + Expenditure_lag_2 + 
##     Expenditure_lag_3 + Expenditure_lag_4 + Expenditure_lag_5 + 
##     Expenditure_lag_6 + Real_lag_1 + Real_lag_2 + Real_lag_3 + 
##     Real_lag_4 + Real_lag_5 + Real_lag_6 + Expectation_lag_1 + 
##     Expectation_lag_2 + Expectation_lag_3 + Expectation_lag_4 + 
##     Expectation_lag_5 + Expectation_lag_6 + Interest_lag_1 + 
##     Interest_lag_2 + Interest_lag_3 + Interest_lag_4 + Interest_lag_5 + 
##     Interest_lag_6 + Rate_lag_1 + Rate_lag_2 + Rate_lag_3 + Rate_lag_4 + 
##     Rate_lag_5 + Rate_lag_6 + Year + Month) - Date
## 
##                     Df Sum of Sq        RSS    AIC
## - Expectation_lag_4  1      8575  571116230 753.87
## - Rate_lag_4         1     29656  571137311 753.87
## - Expenditure_lag_6  1    102514  571210169 753.88
## - Rate_lag_5         1    923231  572030886 753.94
## - Real_lag_3         1   4527097  575634752 754.20
## - Interest_lag_2     1   8869829  579977484 754.51
## - Expenditure_lag_3  1  21822278  592929933 755.44
## <none>                            571107655 755.87
## - Interest_lag_6     1  29547300  600654955 755.99
## - Rate_lag_2         1  34014171  605121826 756.30
## - Real_lag_1         1  39642451  610750106 756.69
## - Month              1  53253437  624361092 757.61
## - Expenditure_lag_5  1  63342072  634449727 758.29
## - Rate_lag_1         1  95395698  666503353 760.36
## - Interest_lag_1     1  97815466  668923121 760.51
## - Year               1 110594463  681702118 761.30
## - Rate_lag_6         1 125093162  696200817 762.19
## - Interest_lag_4     1 126858509  697966164 762.29
## - Interest_lag_3     1 128984883  700092539 762.42
## - Real_lag_4         1 134775303  705882958 762.77
## - Expenditure_lag_2  1 136083936  707191591 762.84
## - Rate_lag_3         1 146596673  717704328 763.46
## - Expectation_lag_5  1 210575658  781683313 767.05
## - Expenditure_lag_4  1 218871708  789979363 767.49
## - Expectation_lag_3  1 222561131  793668786 767.69
## - Expectation_lag_6  1 279151630  850259285 770.58
## - Expectation_lag_1  1 299532992  870640647 771.58
## - Interest_lag_5     1 340681597  911789252 773.52
## - Expenditure_lag_1  1 376463782  947571437 775.13
## - Real_lag_2         1 404646737  975754392 776.36
## - Real_lag_5         1 449681396 1020789051 778.26
## - Real_lag_6         1 463581996 1034689651 778.83
## - Expectation_lag_2  1 473925818 1045033473 779.25
## 
## Step:  AIC=753.87
## Expenditure ~ Expenditure_lag_1 + Expenditure_lag_2 + Expenditure_lag_3 + 
##     Expenditure_lag_4 + Expenditure_lag_5 + Expenditure_lag_6 + 
##     Real_lag_1 + Real_lag_2 + Real_lag_3 + Real_lag_4 + Real_lag_5 + 
##     Real_lag_6 + Expectation_lag_1 + Expectation_lag_2 + Expectation_lag_3 + 
##     Expectation_lag_5 + Expectation_lag_6 + Interest_lag_1 + 
##     Interest_lag_2 + Interest_lag_3 + Interest_lag_4 + Interest_lag_5 + 
##     Interest_lag_6 + Rate_lag_1 + Rate_lag_2 + Rate_lag_3 + Rate_lag_4 + 
##     Rate_lag_5 + Rate_lag_6 + Year + Month
## 
##                     Df Sum of Sq        RSS    AIC
## - Rate_lag_4         1     24545  571140775 751.87
## - Expenditure_lag_6  1     93943  571210173 751.88
## - Rate_lag_5         1   1084669  572200899 751.95
## - Real_lag_3         1   5041940  576158169 752.24
## - Interest_lag_2     1  14726253  585842483 752.94
## - Expenditure_lag_3  1  21814399  592930628 753.44
## <none>                            571116230 753.87
## - Interest_lag_6     1  30867706  601983936 754.08
## - Rate_lag_2         1  36930799  608047029 754.50
## - Real_lag_1         1  60175158  631291388 756.08
## - Expenditure_lag_5  1  63665534  634781763 756.31
## - Month              1  76611983  647728213 757.16
## - Interest_lag_1     1 102984186  674100416 758.83
## - Rate_lag_1         1 117960770  689077000 759.75
## - Rate_lag_6         1 128889779  700006009 760.42
## - Expenditure_lag_2  1 137852458  708968688 760.95
## - Interest_lag_4     1 138290753  709406982 760.98
## - Interest_lag_3     1 138937952  710054181 761.01
## - Rate_lag_3         1 146592594  717708823 761.46
## - Year               1 147103781  718220010 761.49
## - Expectation_lag_5  1 210859280  781975510 765.07
## - Expenditure_lag_4  1 219922501  791038731 765.55
## - Expectation_lag_3  1 224245467  795361697 765.78
## - Real_lag_4         1 276284689  847400919 768.44
## - Expectation_lag_6  1 312988751  884104981 770.22
## - Expectation_lag_1  1 313552838  884669068 770.25
## - Interest_lag_5     1 351898869  923015098 772.03
## - Real_lag_2         1 410891660  982007890 774.63
## - Real_lag_5         1 450439246 1021555476 776.29
## - Real_lag_6         1 538083479 1109199709 779.75
## - Expectation_lag_2  1 615470780 1186587009 782.58
## - Expenditure_lag_1  1 651743316 1222859545 783.85
## 
## Step:  AIC=751.87
## Expenditure ~ Expenditure_lag_1 + Expenditure_lag_2 + Expenditure_lag_3 + 
##     Expenditure_lag_4 + Expenditure_lag_5 + Expenditure_lag_6 + 
##     Real_lag_1 + Real_lag_2 + Real_lag_3 + Real_lag_4 + Real_lag_5 + 
##     Real_lag_6 + Expectation_lag_1 + Expectation_lag_2 + Expectation_lag_3 + 
##     Expectation_lag_5 + Expectation_lag_6 + Interest_lag_1 + 
##     Interest_lag_2 + Interest_lag_3 + Interest_lag_4 + Interest_lag_5 + 
##     Interest_lag_6 + Rate_lag_1 + Rate_lag_2 + Rate_lag_3 + Rate_lag_5 + 
##     Rate_lag_6 + Year + Month
## 
##                     Df Sum of Sq        RSS    AIC
## - Expenditure_lag_6  1    219382  571360157 749.89
## - Rate_lag_5         1   1582888  572723663 749.99
## - Real_lag_3         1   5081037  576221812 750.24
## - Interest_lag_2     1  16228407  587369182 751.05
## <none>                            571140775 751.87
## - Expenditure_lag_3  1  28306679  599447454 751.90
## - Interest_lag_6     1  31868717  603009492 752.15
## - Rate_lag_2         1  52184117  623324892 753.54
## - Real_lag_1         1  66319147  637459922 754.48
## - Month              1  76609469  647750244 755.16
## - Expenditure_lag_5  1  89044638  660185414 755.96
## - Interest_lag_1     1 105602663  676743438 757.00
## - Rate_lag_6         1 132601819  703742595 758.64
## - Year               1 147961088  719101863 759.55
## - Rate_lag_1         1 169904024  741044799 760.81
## - Interest_lag_3     1 170492433  741633208 760.84
## - Expenditure_lag_2  1 181282235  752423010 761.45
## - Interest_lag_4     1 186060554  757201329 761.71
## - Rate_lag_3         1 195136418  766277194 762.21
## - Expectation_lag_5  1 221000699  792141474 763.61
## - Expectation_lag_3  1 242792362  813933138 764.75
## - Expenditure_lag_4  1 250681419  821822194 765.15
## - Expectation_lag_6  1 313037204  884177979 768.22
## - Interest_lag_5     1 366781743  937922518 770.70
## - Expectation_lag_1  1 368100294  939241069 770.76
## - Real_lag_2         1 417189716  988330491 772.90
## - Real_lag_4         1 429221899 1000362674 773.41
## - Real_lag_5         1 450503737 1021644512 774.29
## - Real_lag_6         1 579633838 1150774613 779.29
## - Expectation_lag_2  1 625525107 1196665882 780.94
## - Expenditure_lag_1  1 652104015 1223244790 781.86
## 
## Step:  AIC=749.89
## Expenditure ~ Expenditure_lag_1 + Expenditure_lag_2 + Expenditure_lag_3 + 
##     Expenditure_lag_4 + Expenditure_lag_5 + Real_lag_1 + Real_lag_2 + 
##     Real_lag_3 + Real_lag_4 + Real_lag_5 + Real_lag_6 + Expectation_lag_1 + 
##     Expectation_lag_2 + Expectation_lag_3 + Expectation_lag_5 + 
##     Expectation_lag_6 + Interest_lag_1 + Interest_lag_2 + Interest_lag_3 + 
##     Interest_lag_4 + Interest_lag_5 + Interest_lag_6 + Rate_lag_1 + 
##     Rate_lag_2 + Rate_lag_3 + Rate_lag_5 + Rate_lag_6 + Year + 
##     Month
## 
##                     Df Sum of Sq        RSS    AIC
## - Rate_lag_5         1   1566759  572926916 748.00
## - Real_lag_3         1   4863052  576223209 748.24
## - Interest_lag_2     1  20676194  592036351 749.38
## <none>                            571360157 749.89
## - Interest_lag_6     1  37847112  609207269 750.58
## - Expenditure_lag_3  1  39028312  610388470 750.66
## - Rate_lag_2         1  54421830  625781987 751.71
## - Real_lag_1         1  72050653  643410810 752.87
## - Month              1  81032584  652392741 753.46
## - Interest_lag_1     1 105390382  676750539 755.00
## - Rate_lag_6         1 136921916  708282074 756.91
## - Year               1 149557095  720917252 757.65
## - Rate_lag_1         1 170816948  742177105 758.87
## - Interest_lag_3     1 177657272  749017429 759.26
## - Expenditure_lag_5  1 207139313  778499470 760.88
## - Interest_lag_4     1 211512393  782872550 761.11
## - Expectation_lag_5  1 236140413  807500570 762.41
## - Expenditure_lag_2  1 241520654  812880811 762.69
## - Expectation_lag_3  1 273771014  845131171 764.33
## - Rate_lag_3         1 285234627  856594784 764.89
## - Expectation_lag_6  1 326780309  898140466 766.88
## - Interest_lag_5     1 379341113  950701270 769.27
## - Expenditure_lag_4  1 387445882  958806039 769.63
## - Expectation_lag_1  1 400987657  972347814 770.22
## - Real_lag_5         1 456301684 1027661841 772.54
## - Real_lag_4         1 506142113 1077502270 774.53
## - Real_lag_2         1 544269957 1115630114 775.99
## - Expectation_lag_2  1 643091586 1214451743 779.56
## - Expenditure_lag_1  1 651889874 1223250031 779.86
## - Real_lag_6         1 660722351 1232082508 780.16
## 
## Step:  AIC=748
## Expenditure ~ Expenditure_lag_1 + Expenditure_lag_2 + Expenditure_lag_3 + 
##     Expenditure_lag_4 + Expenditure_lag_5 + Real_lag_1 + Real_lag_2 + 
##     Real_lag_3 + Real_lag_4 + Real_lag_5 + Real_lag_6 + Expectation_lag_1 + 
##     Expectation_lag_2 + Expectation_lag_3 + Expectation_lag_5 + 
##     Expectation_lag_6 + Interest_lag_1 + Interest_lag_2 + Interest_lag_3 + 
##     Interest_lag_4 + Interest_lag_5 + Interest_lag_6 + Rate_lag_1 + 
##     Rate_lag_2 + Rate_lag_3 + Rate_lag_6 + Year + Month
## 
##                     Df Sum of Sq        RSS    AIC
## - Real_lag_3         1   4244503  577171419 746.31
## - Interest_lag_2     1  21538566  594465481 747.55
## <none>                            572926916 748.00
## - Interest_lag_6     1  36466283  609393199 748.59
## - Expenditure_lag_3  1  41278436  614205351 748.92
## - Rate_lag_2         1  53445408  626372324 749.75
## - Real_lag_1         1  71373558  644300474 750.93
## - Month              1  84213113  657140029 751.76
## - Interest_lag_1     1 106412200  679339116 753.16
## - Year               1 158806567  731733482 756.28
## - Interest_lag_3     1 180514440  753441356 757.50
## - Interest_lag_4     1 219843853  792770769 759.64
## - Rate_lag_1         1 229178956  802105872 760.13
## - Expenditure_lag_2  1 240405826  813332742 760.72
## - Expectation_lag_5  1 241451031  814377947 760.77
## - Expectation_lag_3  1 284332859  857259775 762.93
## - Rate_lag_3         1 298471654  871398570 763.61
## - Expenditure_lag_5  1 320147855  893074771 764.65
## - Expectation_lag_6  1 350024878  922951794 766.03
## - Expenditure_lag_4  1 396598667  969525583 768.10
## - Expectation_lag_1  1 423690933  996617848 769.25
## - Rate_lag_6         1 424656169  997583085 769.29
## - Real_lag_5         1 479239626 1052166542 771.53
## - Real_lag_2         1 571074170 1144001085 775.05
## - Interest_lag_5     1 656824155 1229751071 778.08
## - Expectation_lag_2  1 671499601 1244426517 778.58
## - Real_lag_6         1 674360146 1247287062 778.68
## - Expenditure_lag_1  1 718709674 1291636589 780.14
## - Real_lag_4         1 730083573 1303010488 780.51
## 
## Step:  AIC=746.31
## Expenditure ~ Expenditure_lag_1 + Expenditure_lag_2 + Expenditure_lag_3 + 
##     Expenditure_lag_4 + Expenditure_lag_5 + Real_lag_1 + Real_lag_2 + 
##     Real_lag_4 + Real_lag_5 + Real_lag_6 + Expectation_lag_1 + 
##     Expectation_lag_2 + Expectation_lag_3 + Expectation_lag_5 + 
##     Expectation_lag_6 + Interest_lag_1 + Interest_lag_2 + Interest_lag_3 + 
##     Interest_lag_4 + Interest_lag_5 + Interest_lag_6 + Rate_lag_1 + 
##     Rate_lag_2 + Rate_lag_3 + Rate_lag_6 + Year + Month
## 
##                     Df  Sum of Sq        RSS    AIC
## - Interest_lag_2     1   23966294  601137714 746.02
## <none>                             577171419 746.31
## - Interest_lag_6     1   32291430  609462849 746.60
## - Expenditure_lag_3  1   51646374  628817794 747.91
## - Rate_lag_2         1   54260202  631431621 748.08
## - Real_lag_1         1   68794624  645966044 749.04
## - Month              1  101882526  679053945 751.14
## - Interest_lag_1     1  112562221  689733640 751.79
## - Interest_lag_3     1  181636732  758808151 755.80
## - Year               1  204505021  781676440 757.05
## - Expenditure_lag_2  1  258096290  835267709 759.83
## - Interest_lag_4     1  269516051  846687470 760.41
## - Expectation_lag_5  1  274709845  851881264 760.66
## - Expectation_lag_3  1  289612249  866783668 761.39
## - Expenditure_lag_5  1  316309783  893481202 762.66
## - Rate_lag_1         1  320323340  897494759 762.85
## - Rate_lag_3         1  389616816  966788236 765.98
## - Real_lag_5         1  490409812 1067581231 770.14
## - Expectation_lag_1  1  510212550 1087383969 770.91
## - Rate_lag_6         1  546871480 1124042899 772.31
## - Expectation_lag_6  1  572382704 1149554123 773.25
## - Expenditure_lag_4  1  621639355 1198810774 775.01
## - Real_lag_2         1  656647457 1233818876 776.22
## - Interest_lag_5     1  687443706 1264615125 777.26
## - Real_lag_6         1  770433457 1347604876 779.92
## - Real_lag_4         1  830318730 1407490149 781.75
## - Expectation_lag_2  1  851019212 1428190631 782.36
## - Expenditure_lag_1  1 1384525297 1961696716 795.70
## 
## Step:  AIC=746.02
## Expenditure ~ Expenditure_lag_1 + Expenditure_lag_2 + Expenditure_lag_3 + 
##     Expenditure_lag_4 + Expenditure_lag_5 + Real_lag_1 + Real_lag_2 + 
##     Real_lag_4 + Real_lag_5 + Real_lag_6 + Expectation_lag_1 + 
##     Expectation_lag_2 + Expectation_lag_3 + Expectation_lag_5 + 
##     Expectation_lag_6 + Interest_lag_1 + Interest_lag_3 + Interest_lag_4 + 
##     Interest_lag_5 + Interest_lag_6 + Rate_lag_1 + Rate_lag_2 + 
##     Rate_lag_3 + Rate_lag_6 + Year + Month
## 
##                     Df  Sum of Sq        RSS    AIC
## <none>                             601137714 746.02
## - Rate_lag_2         1   37785511  638923225 746.58
## - Interest_lag_6     1   41031779  642169493 746.79
## - Expenditure_lag_3  1   58048032  659185745 747.89
## - Real_lag_1         1   85769465  686907178 749.62
## - Interest_lag_1     1   92346727  693484440 750.02
## - Month              1  101202513  702340226 750.55
## - Year               1  202647481  803785194 756.22
## - Expectation_lag_3  1  273749482  874887195 759.78
## - Expenditure_lag_2  1  292235708  893373422 760.66
## - Expenditure_lag_5  1  305811598  906949311 761.29
## - Interest_lag_4     1  307322886  908460599 761.36
## - Expectation_lag_5  1  325871038  927008752 762.21
## - Rate_lag_1         1  329112034  930249748 762.36
## - Interest_lag_3     1  364599743  965737457 763.93
## - Rate_lag_3         1  365769203  966906916 763.98
## - Real_lag_5         1  510146829 1111284543 769.83
## - Expectation_lag_1  1  533202543 1134340256 770.69
## - Rate_lag_6         1  600639294 1201777008 773.11
## - Expenditure_lag_4  1  663523927 1264661640 775.26
## - Interest_lag_5     1  724831974 1325969687 777.25
## - Expectation_lag_6  1  729135748 1330273462 777.38
## - Real_lag_2         1  789859018 1390996731 779.26
## - Real_lag_6         1  828475702 1429613415 780.41
## - Real_lag_4         1  840491196 1441628909 780.76
## - Expectation_lag_2  1 1216296644 1817434357 790.49
## - Expenditure_lag_1  1 1414812663 2015950377 794.84
summary(final.model)
## 
## Call:
## lm(formula = Expenditure ~ Expenditure_lag_1 + Expenditure_lag_2 + 
##     Expenditure_lag_3 + Expenditure_lag_4 + Expenditure_lag_5 + 
##     Real_lag_1 + Real_lag_2 + Real_lag_4 + Real_lag_5 + Real_lag_6 + 
##     Expectation_lag_1 + Expectation_lag_2 + Expectation_lag_3 + 
##     Expectation_lag_5 + Expectation_lag_6 + Interest_lag_1 + 
##     Interest_lag_3 + Interest_lag_4 + Interest_lag_5 + Interest_lag_6 + 
##     Rate_lag_1 + Rate_lag_2 + Rate_lag_3 + Rate_lag_6 + Year + 
##     Month, data = house_sales_main)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8728.9 -1946.8   152.1  1847.6  9105.1 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        9.995e+07  4.436e+07   2.253 0.039659 *  
## Expenditure_lag_1  8.991e-01  1.513e-01   5.942  2.7e-05 ***
## Expenditure_lag_2 -6.404e-01  2.372e-01  -2.700 0.016446 *  
## Expenditure_lag_3  2.690e-01  2.235e-01   1.204 0.247427    
## Expenditure_lag_4 -7.711e-01  1.895e-01  -4.069 0.001008 ** 
## Expenditure_lag_5 -4.302e-01  1.557e-01  -2.762 0.014520 *  
## Real_lag_1        -1.362e+03  9.312e+02  -1.463 0.164123    
## Real_lag_2         4.916e+03  1.107e+03   4.439 0.000478 ***
## Real_lag_4        -4.442e+03  9.699e+02  -4.580 0.000361 ***
## Real_lag_5        -4.882e+03  1.368e+03  -3.568 0.002805 ** 
## Real_lag_6         5.086e+03  1.119e+03   4.547 0.000386 ***
## Expectation_lag_1  2.572e+03  7.052e+02   3.648 0.002382 ** 
## Expectation_lag_2 -3.347e+03  6.076e+02  -5.509  6.0e-05 ***
## Expectation_lag_3 -2.143e+03  8.200e+02  -2.614 0.019563 *  
## Expectation_lag_5  2.981e+03  1.045e+03   2.852 0.012128 *  
## Expectation_lag_6 -3.060e+03  7.175e+02  -4.265 0.000677 ***
## Interest_lag_1    -1.501e+03  9.891e+02  -1.518 0.149809    
## Interest_lag_3     4.453e+03  1.476e+03   3.016 0.008681 ** 
## Interest_lag_4    -4.501e+03  1.625e+03  -2.769 0.014322 *  
## Interest_lag_5    -6.850e+03  1.611e+03  -4.253 0.000695 ***
## Interest_lag_6     1.155e+03  1.142e+03   1.012 0.327651    
## Rate_lag_1         2.816e+04  9.828e+03   2.866 0.011786 *  
## Rate_lag_2         1.229e+04  1.266e+04   0.971 0.346944    
## Rate_lag_3        -3.444e+04  1.140e+04  -3.021 0.008596 ** 
## Rate_lag_6         2.855e+04  7.374e+03   3.871 0.001507 ** 
## Year              -4.936e+04  2.195e+04  -2.249 0.039989 *  
## Month             -3.259e+03  2.051e+03  -1.589 0.132887    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6331 on 15 degrees of freedom
## Multiple R-squared:  0.9767, Adjusted R-squared:  0.9362 
## F-statistic: 24.14 on 26 and 15 DF,  p-value: 3.614e-08
#model
#final.model
pred_df=c(21815,14669,10732,14631,24450,25566,67.16521,64.69312,62.11510,64.90300,65.9866,67.83600,87.92016,84.51865,83.80907,78.85528,79.02186,79.50939,17.7350,17.9850,18.3880,18.1925,15.6750,15.0660,7.628235,7.072365,7.393975,7.721065,8.003324,7.873881,2021,4)
pred_df=transpose(data.frame(pred_df))
colnames(pred_df)=colnames(house_sales_main)[3:length(colnames(house_sales_main))]